One-class classification (OCC) is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for OCC.
|Journal||Machine Learning: Science and Technology|
|Publication status||Published - 2023 Mar 1|
Bibliographical noteFunding Information:
This research was supported by the Yonsei University Research Fund of 2022 (2022-22-0124), by the National Research Foundation of Korea (Grant Nos. 2021M3H3A1038085, 2019M3E4A1079666, 2022M3E4A1074591, and 2022M3H3A106307411), and by the KIST Institutional Program (2E31531-22-076).
© 2023 The Author(s). Published by IOP Publishing Ltd.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Artificial Intelligence